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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Phase Transition Pathway Sampling via Swarm Intelligence and Graph Theory.

Li Zhu1, R E Cohen1,2, Timothy A Strobel1

  • 1Geophysical Laboratory, Carnegie Institution for Science, 5251 Broad Branch Road, Northwest, Washington, DC 20015, United States.

The Journal of Physical Chemistry Letters
|July 26, 2019
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Summary

We developed a new computational method, pallas, to predict solid-state phase transformation pathways. This tool efficiently identifies low-energy transition routes, aiding materials discovery and synthesis.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Solid-State Physics

Background:

  • Predicting solid-solid transformations and phase transitions is a significant challenge in materials science.
  • Understanding reaction pathways is crucial for designing new materials and synthesis routes.

Purpose of the Study:

  • To develop and validate a novel computational method for predicting low-energy reaction pathways in solid-state systems.
  • To demonstrate the efficacy of the pallas method in understanding complex phase transformations.

Main Methods:

  • Development of a pathway sampling method integrating swarm intelligence and graph theory.
  • Application of the pallas method to benchmark systems: cadmium selenide (CdSe) and silicon (Si).

Main Results:

  • The pallas method successfully identified known low-energy pathways for CdSe wurtzite to rock-salt transition.
  • A novel, lower-energy pathway for CdSe was discovered.
  • Detailed insights into the complex decompression pathway of Si were provided.

Conclusions:

  • The pallas methodology is an effective tool for understanding solid-state phase transformations.
  • This approach offers valuable insights for materials by design and novel synthesis strategies.